import numpy as np
import itertools

import keras
from keras.datasets import mnist
from keras.preprocessing.image import ImageDataGenerator
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator


def load_data(generator=False, data_size=10000, num_batches=600):
    num_classes = 10

    # input image dimensions
    img_rows, img_cols = 28, 28

    # the data, split between train and test sets
    (x_train, y_train), (x_test, y_test) = mnist.load_data()

    if K.image_data_format() == 'channels_first':
        x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
        x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
        input_shape = (1, img_rows, img_cols)
    else:
        x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
        x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
        input_shape = (img_rows, img_cols, 1)

    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train /= 255
    x_test /= 255

    def shuffled(x, y):
        idx = np.r_[:x.shape[0]]
        np.random.shuffle(idx)
        return x[idx], y[idx]

    x_train, y_train = shuffled(x_train, y_train)
    x_train = x_train[:data_size]
    y_train = y_train[:data_size]
    x_test, y_test = shuffled(x_test, y_test)

    # convert class vectors to binary class matrices
    y_train = keras.utils.to_categorical(y_train, num_classes)
    y_test = keras.utils.to_categorical(y_test, num_classes)
    if generator:
        datagen = ImageDataGenerator()
        return itertools.islice(datagen.flow(x_train, y_train), num_batches)
    return x_train, y_train, x_test, y_test


def make_model(lr=0.01, layer_size=128):
    """Create a Convolutional Nueral Network using Keras."""
    num_classes = 10

    model = Sequential()
    model = Sequential()
    model.add(
        Conv2D(
            32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28,
                                                                    1)))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes, activation='softmax'))

    model.compile(
        loss=keras.losses.categorical_crossentropy,
        optimizer=keras.optimizers.rmsprop(lr=lr, decay=1e-6),
        #                   keras.optimizers.SGD(
        #                       lr=lr, momentum=momentum),
        metrics=['accuracy'])
    return model


def evaluate(model, validation=True):
    x_train, y_train, x_test, y_test = load_data(generator=False)
    data = x_test if validation else x_train
    labels = y_test if validation else y_train

    res = model.evaluate(data, labels)
    return dict(zip(model.metrics_names, res))
